Vehicle automation is linked to various benefits, such as increase in fuel and transport efficiency as well as increase in driving comfort. However, automation also comes with a variety of possible downsides, e.g., loss of situational awareness, loss of skills, and inappropriate trust levels regarding system functionality. Drawbacks differ at different automation levels. As highly automated driving (HAD, level 3) requires the driver to take over the driving task in critical situations within a limited period of time, the need for an appropriate human–machine interface (HMI) arises. To foster adequate and efficient human–machine interaction, this contribution presents a user-centered, iterative approach for HMI evaluation of highly automated truck driving. For HMI evaluation, a driving simulator study [n = 32] using a dynamic truck driving simulator was conducted to let users experience the HMI in a semi-real driving context. Participants rated three HMI concepts, differing in their informational content for HAD regarding acceptance, workload, user experience, and controllability. Results showed that all three HMI concepts achieved good to very good results in these measures. Overall, HMI concepts offering more information to the driver about the HAD system showed significantly higher ratings, depicting the positive effect of additional information on the driver–automation interaction.

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Before autonomous vehicles (AVs; SAE levels 4 and 5) become broadly available, acceptance challenges such as trust and safety concerns must be overcome. In the development of appropriate HMIs that will tackle these challenges, physical and social context play essential roles. Contextual factors thus need to be considered in early prototyping stages. Based on a qualitative semi-systematic literature review and knowledge from our research, this paper elaborates on the value of context-based interface prototyping in the AV domain. It provides a comprehensive overview and a discussion of applicable methods, including physical lab-based prototyping (mock-up, ride simulation with virtual and mixed reality, and immersive video), social context simulation (actors, enactment, items and props, and sound), wizard-of-oz, and experimental vehicles. Finally, the paper discusses factors affecting the impact of prototyping and derives recommendations for the application of prototyping methods in future AV studies.

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